By Jeff Gray, CEO & Co-Founder, Gluware
When we started Gluware over a decade ago, conventional wisdom in networking was simple: configure once, change rarely. The industry viewed frequent network modifications as dangerous – a recipe for cascading outages and operational chaos. When we showed our automation prototype to one of the largest networking vendors, their pushback reflected this mindset: why would anyone want a system that never stopped adjusting configurations?
We stuck to our strategy despite early resistance, confident that networks would require intelligent automation to keep pace with accelerating demands. Sure enough, today’s complex multi-vendor environments, evolving attack vectors, and accelerating IT transformation now require enterprises to constantly secure, remediate, and upgrade their networks. It is a delicate dance where the music is always speeding up.
AI is now pushing those requirements into new territory. We’re entering an era of system-determined self-operating networks, where multiple AI agents make autonomous decisions across infrastructure. This evolution promises transformative improvements in network capabilities, stability and security, but only if we get the governance layer right. For mission-critical infrastructure, responsible AI must be architectural, not an afterthought.
Defining Responsible AI for Mission-Critical Infrastructure
At Gluware, responsible AI means three things: validation, transparency, and strategic architectural design.
First, every AI action must be validated before execution, not after. When AI makes network decisions, those decisions cascade across thousands of devices in milliseconds, with no time for human review. An unvalidated AI recommendation could inadvertently disable security controls or trigger business-disrupting outages before operators can intervene.
Second, AI operations must be transparent and auditable. Network teams need to understand what the AI is doing, why it’s doing it, and what the impact will be. This transparency isn’t a feature, it’s the foundation on which trust is built.
Third, validation must be embedded at the architectural level. This is where most current approaches fall short.
Architecture-First Validation vs. Bolt-On Fixes
The industry’s instinct is to add AI “guardrails” as an afterthought by applying validation rules on top of existing systems. This approach fails because it treats validation as a compliance checkbox rather than a fundamental requirement.
Here’s why architecture matters: Enterprise networks now face multiple AI agents – from observability platforms, service management tools, and security systems – all wanting to modify the same infrastructure simultaneously. Bolt-on validation can’t solve this coordination problem because each AI system validates its own changes independently and without insight into what other agents are doing at the same time.
As enterprises adopt more and more agents across their network infrastructure, this challenge only becomes harder to solve. That’s why we took a fundamentally different approach with Titan.
How Gluware Titan Implements Responsible AI
Gluware Titan embeds validation at the architectural level through our DIAL-powered validation engine, which represents over one million development hours across 305 releases. DIAL serves as the governed layer between all AI agents and infrastructure. Every proposed change from any AI system passes through verification before execution, regardless of which agent initiated it.
This is what we call Gluware’s Intelligent MCP Server: it wraps the open source Model Context Protocol with DIAL technology to perform pre-checks and post-checks before any changes reach production infrastructure. Titan coordinates between competing agents, prevents conflicts, and ensures changes align with network intent. The result is accountable, verifiable automation that network teams can depend on.
We’re not just building AI agents. We’re building the centralized governance framework that makes AI adoption sustainable in systems where the failures of autonomous agents could have life-safety and multi-million-dollar implications.
The race toward AI adoption is inevitable. The question is whether we’ll build the foundations that make AI trustworthy for mission-critical infrastructure. At Gluware, we believe enterprises deserve nothing less.
